Content
Semester 1
Logic of Social Inquiry, 7,5 credits
This course will introduce students to principles of scientific inquiry, while also examining the unique features that distinguish social sciences from other sciences. Students will learn to produce research questions and design research to answer these questions. Attention will be paid to the conceptual distinction between the micro- and macro-levels of social analysis, and how computational approaches can be used to target these levels of analysis.
Behavioural Mechanisms in the Social Sciences, 7,5 credits
Social scientific disciplines have evolved under implicit and explicit assumptions about human cognitive and decision-making processes. This course will examine these assumptions, including those underpinning classical rational choice theory and its extensions. Emerging empirical results in behavioural economics, cognitive science, psychology, and related disciplines that bear on human decision making processes will be examined. Cognitive frameworks that offer alternatives to rational choice theory will be considered and critiqued.
Statistics & Data Science I, 7,5 credits
This course presents students with the key concepts, postulates, and theorems in probability theory, and provides an overview of probability distributions relevant to computational social science research. Students are introduced to programming for data analysis, with a focus on random number generators and simulation. Computer simulations and conventional mathematical approaches are used to explore key results in probability and mathematical statistics.
Statistics & Data Science II, 7,5 credits
This course introduces students to multivariate modelling using linear regression. Extensions and special applications of linear regression models are considered, including models appropriate for causal inference. The underlying assumptions and limits of linear regression models are explored using conventional mathematics and computer simulation.
Semester 2
Discrete Choice Modelling, 7,5 credits
This course provides an overview of statistical models for binary and categorical outcomes that are integral to social network analysis, machine learning, and the analysis of human decision making. The course provides a practical introduction to maximum likelihood approaches to model estimation. Models for binary outcomes are considered, including a discussion of key assumptions and limitations. The underlying framework is extended to models for polytomous outcomes, including McFadden’s multinomial logistic regression model. Computer laboratory sessions explore practical applications and model assumptions.
Agent-Based Modelling, 7,5 credits
This course provides a detailed introduction to agent-based modelling (ABM). The course covers all the steps involved in developing an ABM: theoretical design, implementation, and evaluation. In intensive laboratory sessions, students implement agent-based models using object-oriented programming, carry out computer-based experiments with those models, and consider methods for evaluating the robustness and sensitivity of simulation results.
Social Network Analysis, 7,5 credits
Social network concepts, data structures, and measures are introduced. Statistical models applicable to social network data are explored. In intensive laboratory sessions, students work with real network data, create network visualizations, calculate network statistics, implement statistical models related to network formation and evolution, and simulate networks using these models.
Digital Strategies for Social Science Research, 7,5 credits
This course combines a presentation of data collection and management tools with a reflection on their production and their potential use for research. Students learn how to extract relevant information from online data sources, deal with the mass of data that is extracted, and apply appropriate tools for making sense of the data. Students will engage in intensive laboratory sessions in which they acquire their own digital data and apply statistical methods, including machine learning algorithms, to extract insights.
Semester 3
Inequality and Segregation: Theory and Measurement, 7,5 credits
This course introduces commonly used measures of inequality and segregation employed in social science research. Ideal properties of inequality and segregation measures are examined, and common measures evaluated with respect to these properties. Students engage in computer laboratories to generate measures based on social data and examine the sensitivity of measures to population compositions and other characteristics.
Culture: Theory and Research, 7,5 credits
This course introduces major theories, empirical research, and related literature in the study of cultural production and cultural consumption, with an emphasis on contemporary research using computational designs.
Organizations: Theory and Research, 7,5 credits
This course introduces major theories, empirical research, and related literatures in the study of organizations including organizational demography, organizational decision making, and internal dynamics. Special emphasis is given to research employing computational designs.
Big Data: Social Processes and Ethical Issues, 7,5 credits
This course examines the social processes surrounding the creation, storage and use of large scale digital data sets and related digital platforms. Students examine what populations and what kinds of information are included or excluded in emerging “big” datasets, and how these datasets come about. Ethical issues and dangers related to the use of these troves of data are considered.
Studies Abroad
Students can choose to carry out their third semester of studies abroad. The Faculty of Arts and Sciences gives students the opportunity for exchange studies. Specific routines are established for this purpose. Students wishing to take advantage of this possibility must consult with the programme director to ensure credits will be transferred.
Semester 4
Master’s Thesis 30 credits
The topic of the master’s dissertation is decided together with the supervisor. The master’s thesis shall be written within the main area of study, Sociology. Examination includes completion, presentation and defence of a master’s thesis as well as opposition of another master’s thesis.